Anatomy of a successful startup “pivot “: Step 2: Gathering data

Part 1: Spotting a pivot point.

Our customer usage and behavior was the first set of information we had to gather. 
Like most startups we measured most everything during our alpha & beta release. How long were customers on specific pages, which actions did they click on the most, what they searched for (all “in-application” metrics). We were looking for specific information that gave us a sense of what they were trying to do. This was coupled with some in person but mostly web based demos.
142 people were “actively” using the alpha & beta – (we considered once a week as active use at that time). 
We did some very minor multi-variate testing, some people were given a demo before they were given accounts, whereas most were just sent a link and told “have a go at it”. What we found was most people that were given a demo spend far less time on the application that those that were not. At that point we did not know quite what to make of if. The usual suspects were – you needed some type of video “tour” before you used our application, OR the user experience was too complex (which many customers said was not), OR they were looking for things they did not find
We did provide a direct number for support and an in-application support link. Of the nearly 73 people who were using the application cold (no demo given), only 15 people called or emailed if they got stuck in the first month. 17 people tweeted their problems, even during alpha, although we requested them to no do so.
So clearly we did not have passionate users yet. 
The data was telling us that they were using it for a specific purpose, but we were not sure what purpose that was. But we did notice that many of the searches were for specific people not for “brands, companies or terms”.
Turns out most people who were given a demo, were using it for influencer search, because that’s what we told them we wanted to do. They were using it to understand who was influential in a space OR they were looking to get influencer contact information.
But the people who were not given a demo were searching for brands, company names and generic terms like iPhone etc.
Our demo pitch was to Do It Yourself PR, but for folks who did not know that this was supposed to be a solution to help them identify key conversations and brands and finally find the influential folks.
The second data point as I mentioned was blog topics of the key PR professionals. After analyzing their posts over a 3 month period, we realized most were talking about social media, not about PR itself. 
The third data point was competitors. The companies we considered competition at that time were doing partnerships with social media software companies (noticed from press releases) and nearly 6 webinars a week on their events page were about social media integration with PR. Their top messages were about PR and the usual media database, etc, but their actions were all around social media.
The fourth was our analysis of companies getting funded (which we got from Crunchbase). We looked at the total number of companies funded in web services and SaaS markets and found out that a significant amount of money (4.3% more, relative to other markets) were towards either seed or follow on rounds at companies doing social media monitoring.
The final portion of data was twitter conversation analysis. We tracked multiple terms – PR, Public Relations, social media, Blogger outreach, Media database and Word of mouth. It revealed what we gathered from all other sources – social media ( specifically monitoring, analysis and brand monitoring) were the most mentioned terms.
I collated all this in a 9 page deck, which I was ready to share with the engineering team. Which lead us to the final step – communicating the Pivot to customers, internal employees and investors.
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